System and method for using artificial intelligence in telemedicine-enabled hardware to optimize rehabilitative routines capable of enabling remote rehabilitative compliance

ABSTRACT

A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 17/021,895, filed Sep. 15, 2020, titled “Telemedicine forOrthopedic Treatment,” which claims priority to and the benefit of U.S.Provisional Patent Application Ser. No. 62/910,232, filed Oct. 3, 2019,titled “Telemedicine for Orthopedic Treatment,” the entire disclosuresof which are hereby incorporated by reference for all purposes. Thisapplication also claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 63/113,484, filed Nov. 13, 2020, titled“System and Method for Use of Artificial Intelligence inTelemedicine-Enabled Hardware to Optimize Rehabilitative Routines forEnabling Remote Rehabilitative Compliance,” the entire disclosure ofwhich is hereby incorporated by reference for all purposes.

BACKGROUND

Remote medical assistance, also referred to, inter alia, as remotemedicine, telemedicine, telemed, telmed, tel-med, or telehealth, is anat least two-way communication between a healthcare provider orproviders, such as a physician or a physical therapist, and a patientusing audio and/or audiovisual and/or other sensorial or perceptive(e.g., tactile, gustatory, haptic, pressure-sensing-based orelectromagnetic (e.g., neurostimulation) communications (e.g., via acomputer, a smartphone, or a tablet). Telemedicine may aid a patient inperforming various aspects of a rehabilitation regimen for a body part.The patient may use a patient interface in communication with anassistant interface for receiving the remote medical assistance viaaudio, visual, audiovisual, or other communications described elsewhereherein. Any reference herein to any particular sensorial modality shallbe understood to include and to disclose by implication a different oneor more sensory modalities.

Telemedicine is an option for healthcare providers to communicate withpatients and provide patient care when the patients do not want to orcannot easily go to the healthcare providers' offices. Telemedicine,however, has substantive limitations as the healthcare providers cannotconduct physical examinations of the patients. Rather, the healthcareproviders must rely on verbal communication and/or limited remoteobservation of the patients.

SUMMARY

An aspect of the disclosed embodiments includes a computer-implementedsystem. The computer-implemented system comprises a treatment apparatus,a patient interface, and a processing device. The treatment apparatus isconfigured to be manipulated by a user while performing a treatmentplan. The patient interface comprises an output device configured topresent telemedicine information associated with a telemedicine session.The processing device is configured to receive treatment data pertainingto the user during the telemedicine session, wherein the treatment datacomprises one or more characteristics of the user; determine, via one ormore trained machine learning models, at least one respective measure ofbenefit one or more exercise regimens provide the user, wherein thedetermining the respective measure of benefit is based on the treatmentdata; determine, via the one or more trained machine learning models,one or more probabilities of the user complying with the one or moreexercise regimens; and transmit the treatment plan to a computingdevice, wherein the treatment plan is generated based on the one or moreprobabilities and the respective measure of benefit the one or moreexercise regimens provide the user.

Another aspect of the disclosed embodiments includes acomputer-implemented method for optimizing a treatment plan for a userto perform and wherein the performance uses a treatment apparatus. Themethod may include receiving treatment data pertaining to the user. Thetreatment data may include one or more characteristics of the user. Themethod may include determining, via one or more trained machine learningmodels, a respective measure of benefit with which one or more exerciseregimens may provide the user. The term “measure” may refer to anysuitable metric for measuring a benefit that an exercise regimenprovides to a user, and may refer to any suitable value, number,qualitative indicator, score, unit, etc. The term “benefit” may refer toany suitable benefit that an exercise regimen may provide to the user,and may refer to strength, flexibility, pliability, heartrate, bloodpressure, a state of mind (e.g., mood, stress, etc.), physiologicaldata, rehabilitation, a range of motion, endurance, dexterity, etc. Themeasure of benefit may be positive or negative. For example, a firstexercise regimen may be determined to increase the strength of a bodypart of a patient by a certain amount, and thus the measure of benefitfor the first exercise regimen may be a positive measure correspondingto that amount of strength increase. In another example, a secondexercise regimen may be determined to decrease the strength (e.g.,overexertion or overextension of the body part) of a body part of apatient by a certain amount, and thus the measure of benefit for thesecond exercise regimen may be a negative measure corresponding to thatamount of strength decrease. The determining the respective measure ofbenefit is based on the treatment data. The method may includedetermining, via the one or more trained machine learning models, one ormore probabilities associated with the user complying with the one ormore exercise regimens. The method may include transmitting a treatmentplan to a computing device. In one embodiment, the treatment plan isgenerated based on the one or more probabilities and the respectivemeasure of benefit the one or more exercise regimens provide the user.

Another aspect of the disclosed embodiments includes a system thatincludes a processing device and a memory communicatively coupled to theprocessing device and capable of storing instructions. The processingdevice executes the instructions to perform any of the methods,operations, or steps described herein.

Another aspect of the disclosed embodiments includes a tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to perform any of the methods,operations, or steps disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

For a detailed description of example embodiments, reference will now bemade to the accompanying drawings in which:

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer implemented system for managing a treatment plan according tothe principles of the present disclosure;

FIG. 2 generally illustrates a perspective view of an embodiment of atreatment apparatus according to the principles of the presentdisclosure;

FIG. 3 generally illustrates a perspective view of a pedal of thetreatment apparatus of FIG. 2 according to the principles of the presentdisclosure;

FIG. 4 generally illustrates a perspective view of a person using thetreatment apparatus of FIG. 2 according to the principles of the presentdisclosure;

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to the principles of thepresent disclosure;

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to the principles ofthe present disclosure;

FIG. 7 generally illustrates an embodiment of an overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe principles of the present disclosure;

FIG. 8 generally illustrates an example embodiment of a method foroptimizing a treatment plan for a user to increase a probability of theuser complying with the treatment plan according to the principles ofthe present disclosure;

FIG. 9 generally illustrates an example embodiment of a method forgenerating a treatment plan based on a desired benefit, a desired painlevel, an indication of probability of complying with a particularexercise regimen, or some combination thereof according to theprinciples of the present disclosure;

FIG. 10 generally illustrates an example embodiment of a method forcontrolling, based on a treatment plan, a treatment apparatus while auser uses the treatment apparatus according to the principles of thepresent disclosure; and

FIG. 11 generally illustrates an example computer system according tothe principles of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,”“contained within,” “superimposing upon,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols orexercise regimens, and each treatment protocol or exercise regimenincludes one or more treatment sessions or one or more exercisesessions. Each treatment session or exercise session comprises one ormore session periods or exercise periods, with each session period orexercise period including at least one exercise for treating the bodypart of the patient. Any suitable exercise (e.g., muscular, weightlifting, cardiovascular, therapeutic, neuromuscular, neurocognitive,meditating, yoga, stretching, etc.) may be included in a session periodor an exercise period. For example, a treatment plan for post-operativerehabilitation after a knee surgery may include an initial treatmentprotocol or exercise regimen with twice daily stretching sessions forthe first 3 days after surgery and a more intensive treatment protocolwith active exercise sessions performed 4 times per day starting 4 daysafter surgery. A treatment plan may also include information pertainingto a medical procedure to perform on the patient, a treatment protocolfor the patient using a treatment apparatus, a diet regimen for thepatient, a medication regimen for the patient, a sleep regimen for thepatient, additional regimens, or some combination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic,telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “optimal treatment plan” may refer to optimizing a treatmentplan based on a certain parameter or factors or combinations of morethan one parameter or factor, such as, but not limited to, a measure ofbenefit which one or more exercise regimens provide to users, one ormore probabilities of users complying with one or more exerciseregimens, an amount, quality or other measure of sleep associated withthe user, information pertaining to a diet of the user, informationpertaining to an eating schedule of the user, information pertaining toan age of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user, anindication of an energy level of the user, information pertaining to amicrobiome from one or more locations on or in the user (e.g., skin,scalp, digestive tract, vascular system, etc.), or some combinationthereof.

As used herein, the term healthcare provider may include a medicalprofessional (e.g., such as a doctor, a nurse, a therapist, and thelike), an exercise professional (e.g., such as a coach, a trainer, anutritionist, and the like), or another professional sharing at leastone of medical and exercise attributes (e.g., such as an exercisephysiologist, a physical therapist, an occupational therapist, and thelike). As used herein, and without limiting the foregoing, a “healthcareprovider” may be a human being, a robot, a virtual assistant, a virtualassistant in virtual and/or augmented reality, or an artificiallyintelligent entity, such entity including a software program, integratedsoftware and hardware, or hardware alone.

Real-time may refer to less than or equal to 2 seconds. Near real-timemay refer to any interaction of a sufficiently short time to enable twoindividuals to engage in a dialogue via such user interface, and willpreferably but not determinatively be less than 10 seconds but greaterthan 2 seconds.

Any of the systems and methods described in this disclosure may be usedin connection with rehabilitation. Rehabilitation may be directed atcardiac rehabilitation, rehabilitation from stroke, multiple sclerosis,Parkinson's disease, myasthenia gravis, Alzheimer's disease, any otherneurodegenative or neuromuscular disease, a brain injury, a spinal cordinjury, a spinal cord disease, a joint injury, a joint disease,post-surgical recovery, or the like. Rehabilitation can further involvemuscular contraction in order to improve blood flow and lymphatic flow,engage the brain and nervous system to control and affect a traumatizedarea to increase the speed of healing, reverse or reduce pain (includingarthralgias and myalgias), reverse or reduce stiffness, recover range ofmotion, encourage cardiovascular engagement to stimulate the release ofpain-blocking hormones or to encourage highly oxygenated blood flow toaid in an overall feeling of well-being. Rehabilitation may be providedfor individuals of average weight in reasonably good physical conditionhaving no substantial deformities, as well as for individuals moretypically in need of rehabilitation, such as those who are elderly,obese, subject to disease processes, injured and/or who have a severelylimited range of motion. Unless expressly stated otherwise, is to beunderstood that rehabilitation includes prehabilitation (also referredto as “pre-habilitation” or “prehab”). Prehabilitation may be used as apreventative procedure or as a pre-surgical or pre-treatment procedure.Prehabilitation may include any action performed by or on a patient (ordirected to be performed by or on a patient, including, withoutlimitation, remotely or distally through telemedicine) to, withoutlimitation, prevent or reduce a likelihood of injury (e.g., prior to theoccurrence of the injury); improve recovery time subsequent to surgery;improve strength subsequent to surgery; or any of the foregoing withrespect to any non-surgical clinical treatment plan to be undertaken forthe purpose of ameliorating or mitigating injury, dysfunction, or othernegative consequence of surgical or non-surgical treatment on anyexternal or internal part of a patient's body. For example, a mastectomymay require prehabilitation to strengthen muscles or muscle groupsaffected directly or indirectly by the mastectomy. As a furthernon-limiting example, the removal of an intestinal tumor, the repair ofa hernia, open-heart surgery or other procedures performed on internalorgans or structures, whether to repair those organs or structures, toexcise them or parts of them, to treat them, etc., can require cuttingthrough, dissecting and/or harming numerous muscles and muscle groups inor about, without limitation, the skull or face, the abdomen, the ribsand/or the thoracic cavity, as well as in or about all joints andappendages. Prehabilitation can improve a patient's speed of recovery,measure of quality of life, level of pain, etc. in all the foregoingprocedures. In one embodiment of prehabilitation, a pre-surgicalprocedure or a pre-non-surgical-treatment may include one or more setsof exercises for a patient to perform prior to such procedure ortreatment. Performance of the one or more sets of exercises may berequired in order to qualify for an elective surgery, such as a kneereplacement. The patient may prepare an area of his or her body for thesurgical procedure by performing the one or more sets of exercises,thereby strengthening muscle groups, improving existing muscle memory,reducing pain, reducing stiffness, establishing new muscle memory,enhancing mobility (i.e., improve range of motion), improving bloodflow, and/or the like.

The phrase, and all permutations of the phrase, “respective measure ofbenefit with which one or more exercise regimens may provide the user”(e.g., “measure of benefit,” “respective measures of benefit,” “measuresof benefit,” “measure of exercise regimen benefit,” “exercise regimenbenefit measurement,” etc.) may refer to one or more measures of benefitwith which one or more exercise regimens may provide the user.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining a treatment plan for a patient having certaincharacteristics (e.g., vital-sign or other measurements; performance;demographic; psychographic; geographic; diagnostic; measurement- ortest-based; medically historic; behavioral historic; cognitive;etiologic; cohort-associative; differentially diagnostic; surgical,physically therapeutic, microbiome related, pharmacologic and othertreatment(s) recommended; arterial blood gas and/or oxygenation levelsor percentages; glucose levels; blood oxygen levels; insulin levels;psychographics; etc.) may be a technically challenging problem. Forexample, a multitude of information may be considered when determining atreatment plan, which may result in inefficiencies and inaccuracies inthe treatment plan selection process. In a rehabilitative setting, someof the multitude of information considered may include characteristicsof the patient such as personal information, performance information,and measurement information. The personal information may include, e.g.,demographic, psychographic or other information, such as an age, aweight, a gender, a height, a body mass index, a medical condition, afamilial medication history, an injury, a medical procedure, amedication prescribed, or some combination thereof. The performanceinformation may include, e.g., an elapsed time of using a treatmentapparatus, an amount of force exerted on a portion of the treatmentapparatus, a range of motion achieved on the treatment apparatus, amovement speed of a portion of the treatment apparatus, a duration ofuse of the treatment apparatus, an indication of a plurality of painlevels using the treatment apparatus, or some combination thereof. Themeasurement information may include, e.g., a vital sign, a respirationrate, a heartrate, a temperature, a blood pressure, a glucose level,arterial blood gas and/or oxygenation levels or percentages, or otherbiomarker, or some combination thereof. It may be desirable to processand analyze the characteristics of a multitude of patients, thetreatment plans performed for those patients, and the results of thetreatment plans for those patients.

Further, another technical problem may involve distally treating, via acomputing apparatus during a telemedicine session, a patient from alocation different than a location at which the patient is located. Anadditional technical problem is controlling or enabling, from thedifferent location, the control of a treatment apparatus used by thepatient at the patient's location. Oftentimes, when a patient undergoesrehabilitative surgery (e.g., knee surgery), a healthcare provider mayprescribe a treatment apparatus to the patient to use to perform atreatment protocol at their residence or at any mobile location ortemporary domicile. A healthcare provider may refer to a doctor,physician assistant, nurse, chiropractor, dentist, physical therapist,acupuncturist, physical trainer, or the like. A healthcare provider mayrefer to any person with a credential, license, degree, or the like inthe field of medicine, physical therapy, rehabilitation, or the like.

When the healthcare provider is located in a different location from thepatient and the treatment apparatus, it may be technically challengingfor the healthcare provider to monitor the patient's actual progress (asopposed to relying on the patient's word about their progress) in usingthe treatment apparatus, modify the treatment plan according to thepatient's progress, adapt the treatment apparatus to the personalcharacteristics of the patient as the patient performs the treatmentplan, and the like.

Additionally, or alternatively, a computer-implemented system may beused in connection with a treatment apparatus to treat the patient, forexample, during a telemedicine session. For example, the treatmentapparatus can be configured to be manipulated by a user while the useris performing a treatment plan. The system may include a patientinterface that includes an output device configured to presenttelemedicine information associated with the telemedicine session.During the telemedicine session, the processing device can be configuredto receive treatment data pertaining to the user. The treatment data mayinclude one or more characteristics of the user. The processing devicemay be configured to determine, via one or more trained machine learningmodels, at least one respective measure of benefit which one or moreexercise regimens provide the user. Determining the respective measureof benefit may be based on the treatment data. The processing device maybe configured to determine, via the one or more trained machine learningmodels, one or more probabilities of the user complying with the one ormore exercise regimens. The processing device may be configured totransmit the treatment plan, for example, to a computing device. Thetreatment plan can be generated based on the one or more probabilitiesand the respective measure of benefit which the one or more exerciseregimens provide the user.

Accordingly, systems and methods, such as those described herein, thatreceive treatment data pertaining to the user of the treatment apparatusduring telemedicine session, may be desirable.

In some embodiments, the systems and methods described herein may beconfigured to use a treatment apparatus configured to be manipulated byan individual while performing a treatment plan. The individual mayinclude a user, patient, or other a person using the treatment apparatusto perform various exercises for prehabilitation, rehabilitation,stretch training, and the like. The systems and methods described hereinmay be configured to use and/or provide a patient interface comprisingan output device configured to present telemedicine informationassociated with a telemedicine session.

In some embodiments, during an adaptive telemedicine session, thesystems and methods described herein may be configured to use artificialintelligence and/or machine learning to assign patients to cohorts andto dynamically control a treatment apparatus based on the assignment.The term “adaptive telemedicine” may refer to a telemedicine sessiondynamically adapted based on one or more factors, criteria, parameters,characteristics, or the like. The one or more factors, criteria,parameters, characteristics, or the like may pertain to the user (e.g.,heartrate, blood pressure, perspiration rate, pain level, or the like),the treatment apparatus (e.g., pressure, range of motion, speed ofmotor, etc.), details of the treatment plan, and so forth.

In some embodiments, numerous patients may be prescribed numeroustreatment apparatuses because the numerous patients are recovering fromthe same medical procedure and/or suffering from the same injury. Thenumerous treatment apparatusus may be provided to the numerous patients.The treatment apparatuses may be used by the patients to performtreatment plans in their residences, at gyms, at rehabilitative centers,at hospitals, or at any suitable locations, including permanent ortemporary domiciles.

In some embodiments, the treatment apparatuses may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, any or each of thepersonal information, the performance information, and the measurementinformation may be collected before, during, and/or after a patientperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment apparatus throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment apparatus may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step or set of steps in the treatment plan. Such atechnique may enable the determination of which steps in the treatmentplan lead to desired results (e.g., improved muscle strength, range ofmotion, etc.) and which steps lead to diminishing returns (e.g.,continuing to exercise after 3 minutes actually delays or harmsrecovery).

Data may be collected from the treatment apparatuses and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as the interface of the computing device described herein,a clinician interface, patient interface, or the like) over time as thepatients use the treatment apparatuses to perform the various treatmentplans. The data that may be collected may include the characteristics ofthe patients, the treatment plans performed by the patients, and theresults of the treatment plans. Further, the data may includecharacteristics of the treatment apparatus. The characteristics of thetreatment apparatus may include a make (e.g., identity of entity thatdesigned, manufactured, etc. the treatment apparatus 70) of thetreatment apparatus 70, a model (e.g., model number or other identifierof the model) of the treatment apparatus 70, a year (e.g., year thetreatment apparatus was manufactured) of the treatment apparatus 70,operational parameters (e.g., engine temperature during operation, arespective status of each of one or more sensors included in orassociated with the treatment apparatus 70, vibration measurements ofthe treatment apparatus 70 in operation, measurements of static and/ordynamic forces exerted internally or externally on the treatmentapparatus 70, etc.) of the treatment apparatus 70, settings (e.g., rangeof motion setting, speed setting, required pedal force setting, etc.) ofthe treatment apparatus 70, and the like. The data collected from thetreatment apparatuses, computing devices, characteristics of the user,characteristics of the treatment apparatus, and the like may becollectively referred to as “treatment data” herein.

In some embodiments, the data may be processed to group certain peopleinto cohorts. The people may be grouped by people having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic people having nomedical conditions who perform a treatment plan (e.g., use the treatmentapparatus for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older people who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Insome embodiments, the artificial intelligence engine may be used toidentify trends and/or patterns and to define new cohorts based onachieving desired results from the treatment plans and machine learningmodels associated therewith may be trained to identify such trendsand/or patterns and to recommend and rank the desirability of the newcohorts. For example, the one or more machine learning models may betrained to receive an input of characteristics of a new patient and tooutput a treatment plan for the patient that results in a desiredresult. The machine learning models may match a pattern between thecharacteristics of the new patient and at least one patient of thepatients included in a particular cohort. When a pattern is matched, themachine learning models may assign the new patient to the particularcohort and select the treatment plan associated with the at least onepatient. The artificial intelligence engine may be configured tocontrol, distally and based on the treatment plan, the treatmentapparatus while the new patient uses the treatment apparatus to performthe treatment plan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment apparatus toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for people in the cohort to which thenew patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespeople having characteristics similar to the now-changed characteristicsas the new patient. For example, a clinically obese patient may loseweight and no longer meet the weight criterion for the initial cohort,result in the patient's being reassigned to a different cohort with adifferent weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment apparatus may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan, thetreatment apparatus while the new patient uses the treatment apparatusto perform the treatment plan. Such techniques may provide the technicalsolution of distally controlling a treatment apparatus.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds orany reasonably proximate difference between two different times. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions. The term “medical action(s)” may refer to any suitable actionperformed by the healthcare provider, and such action or actions mayinclude diagnoses, prescription of treatment plans, prescription oftreatment apparatusus, and the making, composing and/or executing ofappointments, telemedicine sessions, prescription of medicines,telephone calls, emails, text messages, and the like.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment apparatus to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient. In someembodiments, the artificial intelligence engine may modify the treatmentplan if the monitored data shows the plan to be inappropriate orcounterproductive for the user.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to ahealthcare provider. The healthcare provider may select a particulartreatment plan for the patient to cause that treatment plan to betransmitted to the patient and/or to control, based on the treatmentplan, the treatment apparatus. In some embodiments, to facilitatetelehealth or telemedicine applications, including remote diagnoses,determination of treatment plans and rehabilitative and/or pharmacologicprescriptions, the artificial intelligence engine may receive and/oroperate distally from the patient and the treatment apparatus.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing apparatus of a healthcare provider.The video may also be accompanied by audio, text and other multimediainformation and/or other sensorial or perceptive (e.g., tactile,gustatory, haptic, pressure-sensing-based or electromagnetic (e.g.,neurostimulation). Real-time may refer to less than or equal to 2seconds. Near real-time may refer to any interaction of a sufficientlyshort time to enable two individuals to engage in a dialogue via suchuser interface, and will generally be less than 10 seconds (or anysuitably proximate difference between two different times) but greaterthan 2 seconds. Presenting the treatment plans generated by theartificial intelligence engine concurrently with a presentation of thepatient video may provide an enhanced user interface because thehealthcare provider may continue to visually and/or otherwisecommunicate with the patient while also reviewing the treatment plans onthe same user interface. The enhanced user interface may improve thehealthcare provider's experience using the computing device and mayencourage the healthcare provider to reuse the user interface. Such atechnique may also reduce computing resources (e.g., processing, memory,network) because the healthcare provider does not have to switch toanother user interface screen to enter a query for a treatment plan torecommend based on the characteristics of the patient. The artificialintelligence engine may be configured to provide, dynamically on thefly, the treatment plans and excluded treatment plans.

In some embodiments, the treatment plan may be modified by a healthcareprovider. For example, certain procedures may be added, modified orremoved. In the telehealth scenario, there are certain procedures thatmay not be performed due to the distal nature of a healthcare providerusing a computing device in a different physical location than apatient.

A technical problem may relate to the information pertaining to thepatient's medical condition being received in disparate formats. Forexample, a server may receive the information pertaining to a medicalcondition of the patient from one or more sources (e.g., from anelectronic medical record (EMR) system, application programminginterface (API), or any suitable system that has information pertainingto the medical condition of the patient). That is, some sources used byvarious healthcare provider entities may be installed on their localcomputing devices and, additionally and/or alternatively, may useproprietary formats. Accordingly, some embodiments of the presentdisclosure may use an API to obtain, via interfaces exposed by APIs usedby the sources, the formats used by the sources. In some embodiments,when information is received from the sources, the API may map andconvert the format used by the sources to a standardized (i.e.,canonical) format, language and/or encoding (“format” as used hereinwill be inclusive of all of these terms) used by the artificialintelligence engine. Further, the information converted to thestandardized format used by the artificial intelligence engine may bestored in a database accessed by the artificial intelligence engine whenthe artificial intelligence engine is performing any of the techniquesdisclosed herein. Using the information converted to a standardizedformat may enable a more accurate determination of the procedures toperform for the patient.

The various embodiments disclosed herein may provide a technicalsolution to the technical problem pertaining to the patient's medicalcondition information being received in disparate formats. For example,a server may receive the information pertaining to a medical conditionof the patient from one or more sources (e.g., from an electronicmedical record (EMR) system, application programming interface (API), orany suitable system that has information pertaining to the medicalcondition of the patient). The information may be converted from theformat used by the sources to the standardized format used by theartificial intelligence engine. Further, the information converted tothe standardized format used by the artificial intelligence engine maybe stored in a database accessed by the artificial intelligence enginewhen performing any of the techniques disclosed herein. The standardizedinformation may enable generating optimal treatment plans, where thegenerating is based on treatment plans associated with the standardizedinformation. The optimal treatment plans may be provided in astandardized format that can be processed by various applications (e.g.,telehealth) executing on various computing devices of healthcareproviders and/or patients.

A technical problem may include a challenge of generating treatmentplans for users, such treatment plans comprising exercises that balancea measure of benefit which the exercise regimens provide to the user andthe probability the user complies with the exercises (or the distinctprobabilities the user complies with each of the one or more exercises).By selecting exercises having higher compliance probabilities for theuser, more efficient treatment plans may be generated, and these mayenable less frequent use of the treatment apparatus and therefore extendthe lifetime or time between recommended maintenance of or neededrepairs to the treatment apparatus. For example, if the userconsistently quits a certain exercise but yet attempts to perform theexercise multiple times thereafter, the treatment apparatus may be usedmore times, and therefore suffer more “wear-and-tear” than if the userfully complies with the exercise regimen the first time. In someembodiments, a technical solution may include using trained machinelearning models to generate treatment plans based on the measure ofbenefit exercise regimens provide users and the probabilities of theusers associated with complying with the exercise regimens, suchinclusion thereby leading to more time-efficient, cost-efficient, andmaintenance-efficient use of the treatment apparatus.

In some embodiments, the treatment apparatus may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare provider may adapt, remotely during atelemedicine session, the treatment apparatus to the needs of thepatient by causing a control instruction to be transmitted from a serverto treatment apparatus. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

FIG. 1 shows a block diagram of a computer-implemented system 10,hereinafter called “the system” for managing a treatment plan. Managingthe treatment plan may include using an artificial intelligence engineto recommend treatment plans and/or provide excluded treatment plansthat should not be recommended to a patient.

The system 10 also includes a server 30 configured to store and toprovide data related to managing the treatment plan. The server 30 mayinclude one or more computers and may take the form of a distributedand/or virtualized computer or computers. The server 30 also includes afirst communication interface 32 configured to communicate with theclinician interface 20 via a first network 34. In some embodiments, thefirst network 34 may include wired and/or wireless network connectionssuch as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC),cellular data network, etc. The server 30 includes a first processor 36and a first machine-readable storage memory 38, which may be called a“memory” for short, holding first instructions 40 for performing thevarious actions of the server 30 for execution by the first processor36. The server 30 is configured to store data regarding the treatmentplan. For example, the memory 38 includes a system data store 42configured to hold system data, such as data pertaining to treatmentplans for treating one or more patients.

The system data store 42 may be configured to store optimal treatmentplans generated based on one or more probabilities of users associatedwith complying with the exercise regimens, and the measure of benefitwith which one or more exercise regimens provide the user. The systemdata store 42 may hold data pertaining to one or more exercises (e.g., atype of exercise, which body part the exercise affects, a duration ofthe exercise, which treatment apparatus to use to perform the exercise,repetitions of the exercise to perform, etc.). When any of thetechniques described herein are being performed, or prior to orthereafter such performance, any of the data stored in the system datastore 42 may be accessed by an artificial intelligence engine 11.

The server 30 may also be configured to store data regarding performanceby a patient in following a treatment plan. For example, the memory 38includes a patient data store 44 configured to hold patient data, suchas data pertaining to the one or more patients, including datarepresenting each patient's performance within the treatment plan. Thepatient data store 44 may hold treatment data pertaining to users overtime, such that historical treatment data is accumulated in the patientdata store 44. The patient data store 44 may hold data pertaining tomeasures of benefit one or more exercises provide to users,probabilities of the users complying with the exercise regimens, and thelike. The exercise regimens may include any suitable number of exercises(e.g., shoulder raises, squats, cardiovascular exercises, sit-ups,curls, etc.) to be performed by the user. When any of the techniquesdescribed herein are being performed, or prior to or thereafter suchperformance, any of the data stored in the patient data store 44 may beaccessed by an artificial intelligence engine 11.

In addition, the determination or identification of: the characteristics(e.g., personal, performance, measurement, etc.) of the users, thetreatment plans followed by the users, the measure of benefits whichexercise regimens provide to the users, the probabilities of the usersassociated with complying with exercise regimens, the level ofcompliance with the treatment plans (e.g., the user completed 4 out of 5exercises in the treatment plans, the user completed 80% of an exercisein the treatment plan, etc.), and the results of the treatment plans mayuse correlations and other statistical or probabilistic measures toenable the partitioning of or to partition the treatment plans intodifferent patient cohort-equivalent databases in the patient data store44. For example, the data for a first cohort of first patients having afirst determined measure of benefit provided by exercise regimens, afirst determined probability of the user associated with complying withexercise regimens, a first similar injury, a first similar medicalcondition, a first similar medical procedure performed, a firsttreatment plan followed by the first patient, and/or a first result ofthe treatment plan, may be stored in a first patient database. The datafor a second cohort of second patients having a second determinedmeasure of benefit provided by exercise regimens, a second determinedprobability of the user associated with complying with exerciseregimens, a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and/or a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic, any combination of characteristics, or any measurescalculation therefrom or thereupon may be used to separate the patientsinto cohorts. In some embodiments, the different cohorts of patients maybe stored in different partitions or volumes of the same database. Thereis no specific limit to the number of different cohorts of patientsallowed, other than as limited by mathematical combinatoric and/orpartition theory.

This measure of exercise benefit data, user compliance probability data,characteristic data, treatment plan data, and results data may beobtained from numerous treatment apparatuses and/or computing devicesover time and stored in the database 44. The measure of exercise benefitdata, user compliance probability data, characteristic data, treatmentplan data, and results data may be correlated in the patient-cohortdatabases in the patient data store 44. The characteristics of the usersmay include personal information, performance information, and/ormeasurement information.

In addition to the historical treatment data, measure of exercisebenefit data, and/or user compliance probability data about other usersstored in the patient cohort-equivalent databases, real-time ornear-real-time information based on the current patient's treatmentdata, measure of exercise benefit data, and/or user complianceprobability data about a current patient being treated may be stored inan appropriate patient cohort-equivalent database. The treatment data,measure of exercise benefit data, and/or user compliance probabilitydata of the patient may be determined to match or be similar to thetreatment data, measure of exercise benefit data, and/or user complianceprobability data of another person in a particular cohort (e.g., a firstcohort “A”, a second cohort “B” or a third cohort “C”, etc.) and thepatient may be assigned to the selected or associated cohort.

In some embodiments, the server 30 may execute the artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign users to certain cohorts based on theirtreatment data, generate treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment apparatus 70, among other things. The machine learning models13 may be trained to generate, based on one or more probabilities of theuser complying with one or more exercise regimens and/or a respectivemeasure of benefit one or more exercise regimens provide the user, atreatment plan at least a subset of the one or more exercises for theuser to perform. The one or more machine learning models 13 may begenerated by the training engine 9 and may be implemented in computerinstructions executable by one or more processing devices of thetraining engine 9 and/or the servers 30. To generate the one or moremachine learning models 13, the training engine 9 may train the one ormore machine learning models 13. The one or more machine learning models13 may be used by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other desired computing device, or anycombination of the above. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of information (e.g.,treatment data, measures of benefits of exercises provide to users,probabilities of users complying with the one or more exercise regimens,etc.) pertaining to users who performed treatment plans using thetreatment apparatus 70, the details (e.g., treatment protocol includingexercises, amount of time to perform the exercises, instructions for thepatient to follow, how often to perform the exercises, a schedule ofexercises, parameters/configurations/settings of the treatment apparatus70 throughout each step of the treatment plan, etc.) of the treatmentplans performed by the users using the treatment apparatus 70, and/orthe results of the treatment plans performed by the users, etc.

The one or more machine learning models 13 may be trained to matchpatterns of treatment data of a user with treatment data of other usersassigned to a particular cohort. The term “match” may refer to an exactmatch, a correlative match, a substantial match, a probabilistic match,etc. The one or more machine learning models 13 may be trained toreceive the treatment data of a patient as input, map the treatment datato the treatment data of users assigned to a cohort, and determine arespective measure of benefit one or more exercise regimens provide tothe user based on the measures of benefit the exercises provided to theusers assigned to the cohort. The one or more machine learning models 13may be trained to receive the treatment data of a patient as input, mapthe treatment data to treatment data of users assigned to a cohort, anddetermine one or more probabilities of the user associated withcomplying with the one or more exercise regimens based on theprobabilities of the users in the cohort associated with complying withthe one or more exercise regimens. The one or more machine learningmodels 13 may also be trained to receive various input (e.g., therespective measure of benefit which one or more exercise regimensprovide the user; the one or more probabilities of the user complyingwith the one or more exercise regimens; an amount, quality or othermeasure of sleep associated with the user; information pertaining to adiet of the user, information pertaining to an eating schedule of theuser; information pertaining to an age of the user, informationpertaining to a sex of the user; information pertaining to a gender ofthe user; an indication of a mental state of the user; informationpertaining to a genetic condition of the user; information pertaining toa disease state of the user; an indication of an energy level of theuser; or some combination thereof), and to output a generated treatmentplan for the patient.

The one or more machine learning models 13 may be trained to matchpatterns of a first set of parameters (e.g., treatment data, measures ofbenefits of exercises provided to users, probabilities of usercompliance associated with the exercises, etc.) with a second set ofparameters associated with an optimal treatment plan. The one or moremachine learning models 13 may be trained to receive the first set ofparameters as input, map the characteristics to the second set ofparameters associated with the optimal treatment plan, and select theoptimal treatment plan. The one or more machine learning models 13 mayalso be trained to control, based on the treatment plan, the treatmentapparatus 70.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output, and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1.

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

Further, in some embodiments, based on subsequent data (e.g., treatmentdata, measures of exercise benefit data, probabilities of usercompliance data, treatment plan result data, etc.) received, the machinelearning models 13 may be continuously or continually updated. Forexample, the machine learning models 13 may include one or more hiddenlayers, weights, nodes, parameters, and the like. As the subsequent datais received, the machine learning models 13 may be updated such that theone or more hidden layers, weights, nodes, parameters, and the like areupdated to match or be computable from patterns found in the subsequentdata. Accordingly, the machine learning models 13 may be re-trained onthe fly as subsequent data is received, and therefore, the machinelearning models 13 may continue to learn.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions and/or other sensorial or perceptive (e.g.,tactile, gustatory, haptic, pressure-sensing-based or electromagnetic(e.g., neurostimulation) communication devices. The output device 54 maycomprise one or more different display screens presenting various dataand/or interfaces or controls for use by the patient. The output device54 may include graphics, which may be presented by a web-based interfaceand/or by a computer program or application (App.). In some embodiments,the patient interface 50 may include functionality provided by orsimilar to existing voice-based assistants such as Siri by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung.

In some embodiments, the output device 54 may present a user interfacethat may present a recommended treatment plan, excluded treatment plan,or the like to the patient. The user interface may include one or moregraphical elements that enable the user to select which treatment planto perform. Responsive to receiving a selection of a graphical element(e.g., “Start” button) associated with a treatment plan via the inputdevice 54, the patient interface 50 may communicate a control signal tothe controller 72 of the treatment apparatus, wherein the control signalcauses the treatment apparatus 70 to begin execution of the selectedtreatment plan. As described below, the control signal may control,based on the selected treatment plan, the treatment apparatus 70 bycausing actuation of the actuator 78 (e.g., cause a motor to driverotation of pedals of the treatment apparatus at a certain speed),causing measurements to be obtained via the sensor 76, or the like. Thepatient interface 50 may communicate, via a local communicationinterface 68, the control signal to the treatment apparatus 70.

As shown in FIG. 1, the patient interface 50 includes a secondcommunication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes a treatment apparatus 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment apparatus 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment apparatus 70 may be any suitablemedical, rehabilitative, therapeutic, etc. apparatus configured to becontrolled distally via another computing device to treat a patientand/or exercise the patient. The treatment apparatus 70 may be anelectromechanical machine including one or more weights, anelectromechanical bicycle, an electromechanical spin-wheel, asmart-mirror, a treadmill, or the like. The body part may include, forexample, a spine, a hand, a foot, a knee, or a shoulder. The body partmay include a part of a joint, a bone, or a muscle group, such as one ormore vertebrae, a tendon, or a ligament. As shown in FIG. 1, thetreatment apparatus 70 includes a controller 72, which may include oneor more processors, computer memory, and/or other components. Thetreatment apparatus 70 also includes a fourth communication interface 74configured to communicate with the patient interface 50 via the localcommunication interface 68. The treatment apparatus 70 also includes oneor more internal sensors 76 and an actuator 78, such as a motor. Theactuator 78 may be used, for example, for moving the patient's body partand/or for resisting forces by the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment apparatus 70 such as, for example, aforce, a position, a speed, a velocity, and/or an acceleration. In someembodiments, the internal sensors 76 may include a position sensorconfigured to measure at least one of a linear motion or an angularmotion of a body part of the patient. For example, an internal sensor 76in the form of a position sensor may measure a distance that the patientis able to move a part of the treatment apparatus 70, where suchdistance may correspond to a range of motion that the patient's bodypart is able to achieve. In some embodiments, the internal sensors 76may include a force sensor configured to measure a force applied by thepatient. For example, an internal sensor 76 in the form of a forcesensor may measure a force or weight the patient is able to apply, usinga particular body part, to the treatment apparatus 70.

The system 10 shown in FIG. 1 also includes an ambulation sensor 82,which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The ambulation sensor 82 maytrack and store a number of steps taken by the patient. In someembodiments, the ambulation sensor 82 may take the form of a wristband,wristwatch, or smart watch. In some embodiments, the ambulation sensor82 may be integrated within a phone, such as a smartphone.

The system 10 shown in FIG. 1 also includes a goniometer 84, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The goniometer 84 measures an angle of thepatient's body part. For example, the goniometer 84 may measure theangle of flex of a patient's knee or elbow or shoulder.

The system 10 shown in FIG. 1 also includes a pressure sensor 86, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The pressure sensor 86 measures an amountof pressure or weight applied by a body part of the patient. Forexample, pressure sensor 86 may measure an amount of force applied by apatient's foot when pedaling a stationary bike.

The system 10 shown in FIG. 1 also includes a supervisory interface 90which may be similar or identical to the clinician interface 20. In someembodiments, the supervisory interface 90 may have enhancedfunctionality beyond what is provided on the clinician interface 20. Thesupervisory interface 90 may be configured for use by a person havingresponsibility for the treatment plan, such as an orthopedic surgeon.

The system 10 shown in FIG. 1 also includes a reporting interface 92which may be similar or identical to the clinician interface 20. In someembodiments, the reporting interface 92 may have less functionality fromwhat is provided on the clinician interface 20. For example, thereporting interface 92 may not have the ability to modify a treatmentplan. Such a reporting interface 92 may be used, for example, by abiller to determine the use of the system 10 for billing purposes. Inanother example, the reporting interface 92 may not have the ability todisplay patient identifiable information, presenting only pseudonymizeddata and/or anonymized data for certain data fields concerning a datasubject and/or for certain data fields concerning a quasi-identifier ofthe data subject. Such a reporting interface 92 may be used, forexample, by a researcher to determine various effects of a treatmentplan on different patients.

The system 10 includes an assistant interface 94 for an assistant, suchas a doctor, a nurse, a physical therapist, or a technician, to remotelycommunicate with the patient interface 50 and/or the treatment apparatus70. Such remote communications may enable the assistant to provideassistance or guidance to a patient using the system 10. Morespecifically, the assistant interface 94 is configured to communicate atelemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b with the patientinterface 50 via a network connection such as, for example, via thefirst network 34 and/or the second network 58. The telemedicine signal96, 97, 98 a, 98 b, 99 a, 99 b comprises one of an audio signal 96, anaudiovisual signal 97, an interface control signal 98 a for controllinga function of the patient interface 50, an interface monitor signal 98 bfor monitoring a status of the patient interface 50, an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentapparatus 70, and/or an apparatus monitor signal 99 b for monitoring astatus of the treatment apparatus 70. In some embodiments, each of thecontrol signals 98 a, 99 a may be unidirectional, conveying commandsfrom the assistant interface 94 to the patient interface 50. In someembodiments, in response to successfully receiving a control signal 98a, 99 a and/or to communicate successful and/or unsuccessfulimplementation of the requested control action, an acknowledgementmessage may be sent from the patient interface 50 to the assistantinterface 94. In some embodiments, each of the monitor signals 98 b, 99b may be unidirectional, status-information commands from the patientinterface 50 to the assistant interface 94. In some embodiments, anacknowledgement message may be sent from the assistant interface 94 tothe patient interface 50 in response to successfully receiving one ofthe monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment apparatus 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a to the treatment apparatus 70 in responseto an apparatus control signal 99 a within the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b from the assistant interface 94. In someembodiments, the assistant interface 94 transmits the apparatus controlsignal 99 a (e.g., control instruction that causes an operatingparameter of the treatment apparatus 70 to change) to the treatmentapparatus 70 via any suitable network disclosed herein.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, theassistant may cause content from the prerecorded source to be played onthe patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the assistant tospeak to a patient via the patient interface 50. In some embodiments,assistant input device 22 may be configured to provide voice-basedfunctionalities, with hardware and/or software configured to interpretspoken instructions by the assistant by using the one or moremicrophones. The assistant input device 22 may include functionalityprovided by or similar to existing voice-based assistants such as Siriby Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. Theassistant input device 22 may include other hardware and/or softwarecomponents. The assistant input device 22 may include one or moregeneral purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the assistant. The assistant display 24 may include graphics,which may be presented by a web-based interface and/or by a computerprogram or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the healthcare provider. In some embodiments, thesystem 10 may be configured to recognize and react to spoken requests orcommands by the patient. For example, in response to a verbal command bythe patient (which may be given in any one of several differentlanguages), the system 10 may automatically initiate a telemedicinesession.

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with the assistant interface 94 via the firstnetwork 34. In some embodiments, the first network 34 may include alocal area network (LAN), such as an Ethernet network.

In some embodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the treatment apparatus 70 may each operate from apatient location geographically separate from a location of theassistant interface 94. For example, the patient interface 50 and thetreatment apparatus 70 may be used as part of an in-home rehabilitationsystem, which may be aided remotely by using the assistant interface 94at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as an assistant remotely from any conventional officeinfrastructure. Such remote work may be performed, for example, wherethe assistant interface 94 takes the form of a computer and/ortelephone. This remote work functionality may allow for work-from-homearrangements that may include part time and/or flexible work hours foran assistant.

FIGS. 2-3 show an embodiment of a treatment apparatus 70. Morespecifically, FIG. 2 shows a treatment apparatus 70 in the form of astationary cycling machine 100, which may be called a stationary bike,for short. The stationary cycling machine 100 includes a set of pedals102 each attached to a pedal arm 104 for rotation about an axle 106. Insome embodiments, and as shown in FIG. 2, the pedals 102 are movable onthe pedal arms 104 in order to adjust a range of motion used by thepatient in pedaling. For example, the pedals being located inwardlytoward the axle 106 corresponds to a smaller range of motion than whenthe pedals are located outwardly away from the axle 106. A pressuresensor 86 is attached to or embedded within one of the pedals 102 formeasuring an amount of force applied by the patient on the pedal 102.The pressure sensor 86 may communicate wirelessly to the treatmentapparatus 70 and/or to the patient interface 50.

FIG. 4 shows a person (a patient) using the treatment apparatus of FIG.2, and showing sensors and various data parameters connected to apatient interface 50. The example patient interface 50 is a tabletcomputer or smartphone, or a phablet, such as an iPad, an iPhone, anAndroid device, or a Surface tablet, which is held manually by thepatient. In some other embodiments, the patient interface 50 may beembedded within or attached to the treatment apparatus 70. FIG. 4 showsthe patient wearing the ambulation sensor 82 on his wrist, with a noteshowing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 hasrecorded and transmitted that step count to the patient interface 50.FIG. 4 also shows the patient wearing the goniometer 84 on his rightknee, with a note showing “KNEE ANGLE 72°”, indicating that thegoniometer 84 is measuring and transmitting that knee angle to thepatient interface 50. FIG. 4 also shows a right side of one of thepedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,”indicating that the right pedal pressure sensor 86 is measuring andtransmitting that force measurement to the patient interface 50. FIG. 4also shows a left side of one of the pedals 102 with a pressure sensor86 showing “FORCE 27 lbs.”, indicating that the left pedal pressuresensor 86 is measuring and transmitting that force measurement to thepatient interface 50. FIG. 4 also shows other patient data, such as anindicator of “SESSION TIME 0:04:13”, indicating that the patient hasbeen using the treatment apparatus 70 for 4 minutes and 13 seconds. Thissession time may be determined by the patient interface 50 based oninformation received from the treatment apparatus 70. FIG. 4 also showsan indicator showing “PAIN LEVEL 3”. Such a pain level may be obtainedfrom the patent in response to a solicitation, such as a question,presented upon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the assistant to remotelyassist a patient with using the patient interface 50 and/or thetreatment apparatus 70. This remote assistance functionality may also becalled telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment apparatus 70. The patient profile display 130 maytake the form of a portion or region of the overview display 120, asshown in FIG. 5, although the patient profile display 130 may take otherforms, such as a separate screen or a popup window. In some embodiments,the patient profile display 130 may include a limited subset of thepatient's biographical information. More specifically, the datapresented upon the patient profile display 130 may depend upon theassistant's need for that information. For example, a healthcareprovider that is assisting the patient with a medical issue may beprovided with medical history information regarding the patient, whereasa technician troubleshooting an issue with the treatment apparatus 70may be provided with a much more limited set of information regardingthe patient. The technician, for example, may be given only thepatient's name. The patient profile display 130 may includepseudonymized data and/or anonymized data or use any privacy enhancingtechnology to prevent confidential patient data from being communicatedin a way that could violate patient confidentiality requirements. Suchprivacy enhancing technologies may enable compliance with laws,regulations, or other rules of governance such as, but not limited to,the Health Insurance Portability and Accountability Act (HIPAA), or theGeneral Data Protection Regulation (GDPR), wherein the patient may bedeemed a “data subject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment apparatus 70. Such treatment plan information may belimited to an assistant who is a healthcare provider, such as a doctoror physical therapist. For example, a healthcare provider assisting thepatient with an issue regarding the treatment regimen may be providedwith treatment plan information, whereas a technician troubleshooting anissue with the treatment apparatus 70 may not be provided with anyinformation regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the assistant. The one or more recommended treatment plans and/orexcluded treatment plans may be generated by the artificial intelligenceengine 11 of the server 30 and received from the server 30 in real-timeduring, inter alia, a telemedicine or telehealth session. An example ofpresenting the one or more recommended treatment plans and/or excludedtreatment plans is described below with reference to FIG. 7.

The example overview display 120 shown in FIG. 5 also includes a patientstatus display 134 presenting status information regarding a patientusing the treatment apparatus. The patient status display 134 may takethe form of a portion or region of the overview display 120, as shown inFIG. 5, although the patient status display 134 may take other forms,such as a separate screen or a popup window. The patient status display134 includes sensor data 136 from one or more of the external sensors82, 84, 86, and/or from one or more internal sensors 76 of the treatmentapparatus 70. In some embodiments, the patient status display 134 mayinclude sensor data from one or more sensors of one or more wearabledevices worn by the patient while using the treatment device 70. The oneor more wearable devices may include a watch, a bracelet, a necklace, achest strap, and the like. The one or more wearable devices may beconfigured to monitor a heartrate, a temperature, a blood pressure, oneor more vital signs, and the like of the patient while the patient isusing the treatment device 70. In some embodiments, the patient statusdisplay 134 may present other data 138 regarding the patient, such aslast reported pain level, or progress within a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the assistant/user's needfor and/or qualifications to view that information.

The example overview display 120 shown in FIG. 5 also includes a helpdata display 140 presenting information for the assistant to use inassisting the patient. The help data display 140 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thehelp data display 140 may take other forms, such as a separate screen ora popup window. The help data display 140 may include, for example,presenting answers to frequently asked questions regarding use of thepatient interface 50 and/or the treatment apparatus 70. The help datadisplay 140 may also include research data or best practices. In someembodiments, the help data display 140 may present scripts for answersor explanations in response to patient questions. In some embodiments,the help data display 140 may present flow charts or walk-throughs forthe assistant to use in determining a root cause and/or solution to apatient's problem. In some embodiments, the assistant interface 94 maypresent two or more help data displays 140, which may be the same ordifferent, for simultaneous presentation of help data for use by theassistant, for example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the assistant to read to the patient, such information to preferablyinclude directions for the patient to perform some action, which mayhelp to narrow down or solve the problem. In some embodiments, basedupon inputs to the troubleshooting flowchart in the first help datadisplay, the second help data display may automatically populate withscript information.

The example overview display 120 shown in FIG. 5 also includes a patientinterface control 150 presenting information regarding the patientinterface 50, and/or to modify one or more settings of the patientinterface 50. The patient interface control 150 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thepatient interface control 150 may take other forms, such as a separatescreen or a popup window. The patient interface control 150 may presentinformation communicated to the assistant interface 94 via one or moreof the interface monitor signals 98 b. As shown in FIG. 5, the patientinterface control 150 includes a display feed 152 of the displaypresented by the patient interface 50. In some embodiments, the displayfeed 152 may include a live copy of the display screen currently beingpresented to the patient by the patient interface 50. In other words,the display feed 152 may present an image of what is presented on adisplay screen of the patient interface 50. In some embodiments, thedisplay feed 152 may include abbreviated information regarding thedisplay screen currently being presented by the patient interface 50,such as a screen name or a screen number. The patient interface control150 may include a patient interface setting control 154 for theassistant to adjust or to control one or more settings or aspects of thepatient interface 50. In some embodiments, the patient interface settingcontrol 154 may cause the assistant interface 94 to generate and/or totransmit an interface control signal 98 for controlling a function or asetting of the patient interface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for theassistant to remotely view and/or control the patient interface 50. Forexample, the patient interface setting control 154 may enable theassistant to remotely enter text to one or more text entry fields on thepatient interface 50 and/or to remotely control a cursor on the patientinterface 50 using a mouse or touchscreen of the assistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the assistant to change asetting that cannot be changed by the patient. For example, the patientinterface 50 may be precluded from accessing a language setting toprevent a patient from inadvertently switching, on the patient interface50, the language used for the displays, whereas the patient interfacesetting control 154 may enable the assistant to change the languagesetting of the patient interface 50. In another example, the patientinterface 50 may not be able to change a font size setting to a smallersize in order to prevent a patient from inadvertently switching the fontsize used for the displays on the patient interface 50 such that thedisplay would become illegible to the patient, whereas the patientinterface setting control 154 may provide for the assistant to changethe font size setting of the patient interface 50.

The example overview display 120 shown in FIG. 5 also includes aninterface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment apparatus 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as shown in FIG. 5. The interface communications display156 may take other forms, such as a separate screen or a popup window.The interface communications display 156 may include controls for theassistant to remotely modify communications with one or more of theother devices 70, 82, 84. For example, the assistant may remotelycommand the patient interface 50 to reset communications with one of theother devices 70, 82, 84, or to establish communications with a new oneof the other devices 70, 82, 84. This functionality may be used, forexample, where the patient has a problem with one of the other devices70, 82, 84, or where the patient receives a new or a replacement one ofthe other devices 70, 82, 84.

The example overview display 120 shown in FIG. 5 also includes anapparatus control 160 for the assistant to view and/or to controlinformation regarding the treatment apparatus 70. The apparatus control160 may take the form of a portion or region of the overview display120, as shown in FIG. 5. The apparatus control 160 may take other forms,such as a separate screen or a popup window. The apparatus control 160may include an apparatus status display 162 with information regardingthe current status of the apparatus. The apparatus status display 162may present information communicated to the assistant interface 94 viaone or more of the apparatus monitor signals 99 b. The apparatus statusdisplay 162 may indicate whether the treatment apparatus 70 is currentlycommunicating with the patient interface 50. The apparatus statusdisplay 162 may present other current and/or historical informationregarding the status of the treatment apparatus 70.

The apparatus control 160 may include an apparatus setting control 164for the assistant to adjust or control one or more aspects of thetreatment apparatus 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentapparatus 70, (e.g., a pedal radius setting, a resistance setting, atarget RPM, other suitable characteristics of the treatment device 70,or a combination thereof).

The apparatus setting control 164 may include a mode button 166 and aposition control 168, which may be used in conjunction for the assistantto place an actuator 78 of the treatment apparatus 70 in a manual mode,after which a setting, such as a position or a speed of the actuator 78,can be changed using the position control 168. The mode button 166 mayprovide for a setting, such as a position, to be toggled betweenautomatic and manual modes. In some embodiments, one or more settingsmay be adjustable at any time, and without having an associatedauto/manual mode. In some embodiments, the assistant may change anoperating parameter of the treatment apparatus 70, such as a pedalradius setting, while the patient is actively using the treatmentapparatus 70. Such “on the fly” adjustment may or may not be availableto the patient using the patient interface 50. In some embodiments, theapparatus setting control 164 may allow the assistant to change asetting that cannot be changed by the patient using the patientinterface 50. For example, the patient interface 50 may be precludedfrom changing a preconfigured setting, such as a height or a tiltsetting of the treatment apparatus 70, whereas the apparatus settingcontrol 164 may provide for the assistant to change the height or tiltsetting of the treatment apparatus 70.

The example overview display 120 shown in FIG. 5 also includes a patientcommunications control 170 for controlling an audio or an audiovisualcommunications session with the patient interface 50. The communicationssession with the patient interface 50 may comprise a live feed from theassistant interface 94 for presentation by the output device of thepatient interface 50. The live feed may take the form of an audio feedand/or a video feed. In some embodiments, the patient interface 50 maybe configured to provide two-way audio or audiovisual communicationswith a person using the assistant interface 94. Specifically, thecommunications session with the patient interface 50 may includebidirectional (two-way) video or audiovisual feeds, with each of thepatient interface 50 and the assistant interface 94 presenting video ofthe other one. In some embodiments, the patient interface 50 may presentvideo from the assistant interface 94, while the assistant interface 94presents only audio or the assistant interface 94 presents no live audioor visual signal from the patient interface 50. In some embodiments, theassistant interface 94 may present video from the patient interface 50,while the patient interface 50 presents only audio or the patientinterface 50 presents no live audio or visual signal from the assistantinterface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as shown in FIG. 5. The patientcommunications control 170 may take other forms, such as a separatescreen or a popup window. The audio and/or audiovisual communicationsmay be processed and/or directed by the assistant interface 94 and/or byanother device or devices, such as a telephone system, or avideoconferencing system used by the assistant while the assistant usesthe assistant interface 94. Alternatively or additionally, the audioand/or audiovisual communications may include communications with athird party. For example, the system 10 may enable the assistant toinitiate a 3-way conversation regarding use of a particular piece ofhardware or software, with the patient and a subject matter expert, suchas a medical professional or a specialist. The example patientcommunications control 170 shown in FIG. 5 includes call controls 172for the assistant to use in managing various aspects of the audio oraudiovisual communications with the patient. The call controls 172include a disconnect button 174 for the assistant to end the audio oraudiovisual communications session. The call controls 172 also include amute button 176 to temporarily silence an audio or audiovisual signalfrom the assistant interface 94. In some embodiments, the call controls172 may include other features, such as a hold button (not shown). Thecall controls 172 also include one or more record/playback controls 178,such as record, play, and pause buttons to control, with the patientinterface 50, recording and/or playback of audio and/or video from theteleconference session (e.g., which may be referred to herein as thevirtual conference room). The call controls 172 also include a videofeed display 180 for presenting still and/or video images from thepatient interface 50, and a self-video display 182 showing the currentimage of the assistant using the assistant interface. The self-videodisplay 182 may be presented as a picture-in-picture format, within asection of the video feed display 180, as shown in FIG. 5. Alternativelyor additionally, the self-video display 182 may be presented separatelyand/or independently from the video feed display 180.

The example overview display 120 shown in FIG. 5 also includes a thirdparty communications control 190 for use in conducting audio and/oraudiovisual communications with a third party. The third partycommunications control 190 may take the form of a portion or region ofthe overview display 120, as shown in FIG. 5. The third partycommunications control 190 may take other forms, such as a display on aseparate screen or a popup window. The third party communicationscontrol 190 may include one or more controls, such as a contact listand/or buttons or controls to contact a third party regarding use of aparticular piece of hardware or software, e.g., a subject matter expert,such as a medical professional or a specialist. The third partycommunications control 190 may include conference calling capability forthe third party to simultaneously communicate with both the assistantvia the assistant interface 94, and with the patient via the patientinterface 50. For example, the system 10 may provide for the assistantto initiate a 3-way conversation with the patient and the third party.

FIG. 6 shows an example block diagram of training a machine learningmodel 13 to output, based on data 600 pertaining to the patient, atreatment plan 602 for the patient according to the present disclosure.Data pertaining to other patients may be received by the server 30. Theother patients may have used various treatment apparatuses to performtreatment plans. The data may include characteristics of the otherpatients, the details of the treatment plans performed by the otherpatients, and/or the results of performing the treatment plans (e.g., apercent of recovery of a portion of the patients' bodies, an amount ofrecovery of a portion of the patients' bodies, an amount of increase ordecrease in muscle strength of a portion of patients' bodies, an amountof increase or decrease in range of motion of a portion of patients'bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the treatment apparatus 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the treatment apparatus 70 are set toX (where X is a numerical value) for the first two weeks and to Y (whereY is a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan that provides the result. Accordingly,when the data 600 for a new patient is input into the trained machinelearning model 13, the trained machine learning model 13 may match thecharacteristics included in the data 600 with characteristics in eithercohort A or cohort B and output the appropriate treatment plan 602. Insome embodiments, the machine learning model 13 may be trained to outputone or more excluded treatment plans that should not be performed by thenew patient.

FIG. 7 shows an embodiment of an overview display 120 of the assistantinterface 94 presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe present disclosure. As depicted, the overview display 120 onlyincludes sections for the patient profile 130 and the video feed display180, including the self-video display 182. Any suitable configuration ofcontrols and interfaces of the overview display 120 described withreference to FIG. 5 may be presented in addition to or instead of thepatient profile 130, the video feed display 180, and the self-videodisplay 182.

The healthcare provider using the assistant interface 94 (e.g.,computing device) during the telemedicine session may be presented inthe self-video 182 in a portion of the overview display 120 (e.g., userinterface presented on a display screen 24 of the assistant interface94) that also presents a video from the patient in the video feeddisplay 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe healthcare provider to share on the patient interface 50, inreal-time or near real-time during the telemedicine session, therecommended treatment plans and/or the excluded treatment plans with thepatient. The healthcare provider may select the GUI object 700 to sharethe recommended treatment plans and/or the excluded treatment plans. Asdepicted, another portion of the overview display 120 includes thepatient profile display 130.

In FIG. 7, the patient profile display 130 is presenting two examplerecommended treatment plans 708 and one example excluded treatment plan710. As described herein, the treatment plans may be recommended basedon the one or more probabilities and the respective measure of benefitthe one or more exercises provide the user. The trained machine learningmodels 13 may (i) use treatment data pertaining to a user to determine arespective measure of benefit which one or more exercise regimensprovide the user, (ii) determine one or more probabilities of the userassociated with complying with the one or more exercise regimens, and(iii) generate, using the one or more probabilities and the respectivemeasure of benefit the one or more exercises provide to the user, thetreatment plan. In some embodiments, the one or more trained machinelearning models 13 may generate treatment plans including exercisesassociated with a certain threshold (e.g., any suitable percentagemetric, value, percentage, number, indicator, probability, etc., whichmay be configurable) associated with the user complying with the one ormore exercise regimens to enable achieving a higher user compliance withthe treatment plan. In some embodiments, the one or more trained machinelearning models 13 may generate treatment plans including exercisesassociated with a certain threshold (e.g., any suitable percentagemetric, value, percentage, number, indicator, probability, etc., whichmay be configurable) associated with one or more measures of benefit theexercises provide to the user to enable achieving the benefits (e.g.,strength, flexibility, range of motion, etc.) at a faster rate, at agreater proportion, etc. In some embodiments, when both the measures ofbenefit and the probability of compliance are considered by the trainedmachine learning models 13, each of the measures of benefit and theprobability of compliance may be associated with a different weight,such different weight causing one to be more influential than the other.Such techniques may enable configuring which parameter (e.g.,probability of compliance or measures of benefit) is more desirable toconsider more heavily during generation of the treatment plan.

For example, as depicted, the patient profile display 130 presents “Thefollowing treatment plans are recommended for the patient based on oneor more probabilities of the user complying with one or more exerciseregimens and the respective measure of benefit the one or more exercisesprovide the user.” Then, the patient profile display 130 presents afirst recommended treatment plan.

As depicted, treatment plan “1” indicates “Patient X should usetreatment apparatus for 30 minutes a day for 4 days to achieve anincreased range of motion of Y %. The exercises include a first exerciseof pedaling the treatment apparatus for 30 minutes at a range of motionof Z % at 5 miles per hour, a second exercise of pedaling the treatmentapparatus for 30 minutes at a range of motion of Y % at 10 miles perhour, etc. The first and second exercise satisfy a threshold complianceprobability and/or a threshold measure of benefit which the exerciseregimens provide to the user.” Accordingly, the treatment plan generatedincludes a first and second exercise, etc. that increase the range ofmotion of Y %. Further, in some embodiments, the exercises are indicatedas satisfying a threshold compliance probability and/or a thresholdmeasure of benefit which the exercise regimens provide to the user. Eachof the exercises may specify any suitable parameter of the exerciseand/or treatment apparatus 70 (e.g., duration of exercise, speed ofmotor of the treatment apparatus 70, range of motion setting of pedals,etc.). This specific example and all such examples elsewhere herein arenot intended to limit in any way the generated treatment plan fromrecommending any suitable number and/or type of exercise.

Recommended treatment plan “2” may specify, based on a desired benefit,an indication of a probability of compliance, or some combinationthereof, and different exercises for the user to perform.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 710. These types of treatment plans are shownto the assistant using the assistant interface 94 to alert the assistantnot to recommend certain portions of a treatment plan to the patient.For example, the excluded treatment plan could specify the following:“Patient X should not use treatment apparatus for longer than 30 minutesa day due to a heart condition.” Specifically, the excluded treatmentplan points out a limitation of a treatment protocol where, due to aheart condition, Patient X should not exercise for more than 30 minutesa day. The excluded treatment plans may be based on treatment data(e.g., characteristics of the user, characteristics of the treatmentapparatus 70, or the like).

The assistant may select the treatment plan for the patient on theoverview display 120. For example, the assistant may use an inputperipheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) toselect from the treatment plans 708 for the patient.

In any event, the assistant may select the treatment plan for thepatient to follow to achieve a desired result. The selected treatmentplan may be transmitted to the patient interface 50 for presentation.The patient may view the selected treatment plan on the patientinterface 50. In some embodiments, the assistant and the patient maydiscuss during the telemedicine session the details (e.g., treatmentprotocol using treatment apparatus 70, diet regimen, medication regimen,etc.) in real-time or in near real-time. In some embodiments, asdiscussed further with reference to method 1000 of FIG. 10 below, theserver 30 may control, based on the selected treatment plan and duringthe telemedicine session, the treatment apparatus 70 as the user usesthe treatment apparatus 70.

FIG. 8 shows an example embodiment of a method 800 for optimizing atreatment plan for a user to increase a probability of the usercomplying with the treatment plan according to the present disclosure.The method 800 is performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software (such as is run ona general-purpose computer system or a dedicated machine), or acombination of both. The method 800 and/or each of its individualfunctions, routines, other methods, scripts, subroutines, or operationsmay be performed by one or more processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In certain implementations, the method 800 maybe performed by a single processing thread. Alternatively, the method800 may be performed by two or more processing threads, each threadimplementing one or more individual functions or routines; or othermethods, scripts, subroutines, or operations of the methods.

For simplicity of explanation, the method 800 is depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently, and/or withother operations not presented and described herein. For example, theoperations depicted in the method 800 may occur in combination with anyother operation of any other method disclosed herein. Furthermore, notall illustrated operations may be required to implement the method 800in accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that the method 800could alternatively be represented as a series of interrelated statesvia a state diagram, a directed graph, a deterministic finite stateautomaton, a non-deterministic finite state automaton, a Markov diagram,or event diagrams.

At 802, the processing device may receive treatment data pertaining to auser (e.g., patient, volunteer, trainee, assistant, healthcare provider,instructor, etc.). The treatment data may include one or morecharacteristics (e.g., vital-sign or other measurements; performance;demographic; psychographic; geographic; diagnostic; measurement- ortest-based; medically historic; etiologic; cohort-associative;differentially diagnostic; surgical, physically therapeutic,pharmacologic and other treatment(s) recommended; arterial blood gasand/or oxygenation levels or percentages; psychographics; etc.) of theuser. The treatment data may include one or more characteristics of thetreatment apparatus 70. In some embodiments, the one or morecharacteristics of the treatment apparatus 70 may include a make (e.g.,identity of entity that designed, manufactured, etc. the treatmentapparatus 70) of the treatment apparatus 70, a model (e.g., model numberor other identifier of the model) of the treatment apparatus 70, a year(e.g., year of manufacturing) of the treatment apparatus 70, operationalparameters (e.g., motor temperature during operation; status of eachsensor included in or associated with the treatment apparatus 70; thepatient, or the environment; vibration measurements of the treatmentapparatus 70 in operation; measurements of static and/or dynamic forcesexerted on the treatment apparatus 70; etc.) of the treatment apparatus70, settings (e.g., range of motion setting; speed setting; requiredpedal force setting; etc.) of the treatment apparatus 70, and the like.In some embodiments, the characteristics of the user and/or thecharacteristics of the treatment apparatus 70 may be tracked over timeto obtain historical data pertaining to the characteristics of the userand/or the treatment apparatus 70. The foregoing embodiments shall alsobe deemed to include the use of any optional internal components or ofany external components attachable to, but separate from the treatmentapparatus itself. “Attachable” as used herein shall be physically,electronically, mechanically, virtually or in an augmented realitymanner.

In some embodiments, when generating a treatment plan, thecharacteristics of the user and/or treatment apparatus 70 may be used.For example, certain exercises may be selected or excluded based on thecharacteristics of the user and/or treatment apparatus 70. For example,if the user has a heart condition, high intensity exercises may beexcluded in a treatment plan. In another example, a characteristic ofthe treatment apparatus 70 may indicate the motor shudders, stalls orotherwise runs improperly at a certain number of revolutions per minute.In order to extend the lifetime of the treatment apparatus 70, thetreatment plan may exclude exercises that include operating the motor atthat certain revolutions per minute or at a prescribed manufacturingtolerance within those certain revolutions per minute.

At 804, the processing device may determine, via one or more trainedmachine learning models 13, a respective measure of benefit with whichone or more exercises provide the user. In some embodiments, based onthe treatment data, the processing device may execute the one or moretrained machine learning models 13 to determine the respective measuresof benefit. For example, the treatment data may include thecharacteristics of the user (e.g., heartrate, vital-sign, medicalcondition, injury, surgery, etc.), and the one or more trained machinelearning models may receive the treatment data and output the respectivemeasure of benefit with which one or more exercises provide the user.For example, if the user has a heart condition, a high intensityexercise may provide a negative benefit to the user, and thus, thetrained machine learning model may output a negative measure of benefitfor the high intensity exercise for the user. In another example, anexercise including pedaling at a certain range of motion may have apositive benefit for a user recovering from a certain surgery, and thus,the trained machine learning model may output a positive measure ofbenefit for the exercise regimen for the user.

At 806, the processing device may determine, via the one or more trainedmachine learning models 13, one or more probabilities associated withthe user complying with the one or more exercise regimens. In someembodiments, the relationship between the one or more probabilitiesassociated with the user complying with the one or more exerciseregimens may be one to one, one to many, many to one, or many to many.The one or more probabilities of compliance may refer to a metric (e.g.,value, percentage, number, indicator, probability, etc.) associated witha probability the user will comply with an exercise regimen. In someembodiments, the processing device may execute the one or more trainedmachine learning models 13 to determine the one or more probabilitiesbased on (i) historical data pertaining to the user, another user, orboth, (ii) received feedback from the user, another user, or both, (iii)received feedback from a treatment apparatus used by the user, or (iv)some combination thereof.

For example, historical data pertaining to the user may indicate ahistory of the user previously performing one or more of the exercises.In some instances, at a first time, the user may perform a firstexercise to completion. At a second time, the user may terminate asecond exercise prior to completion. Feedback data from the user and/orthe treatment apparatus 70 may be obtained before, during, and aftereach exercise performed by the user. The trained machine learning modelmay use any combination of data (e.g., (i) historical data pertaining tothe user, another user, or both, (ii) received feedback from the user,another user, or both, (iii) received feedback from a treatmentapparatus used by the user) described above to learn a user complianceprofile for each of the one or more exercises. The term “user complianceprofile” may refer to a collection of histories of the user complyingwith the one or more exercise regimens. In some embodiments, the trainedmachine learning model may use the user compliance profile, among otherdata (e.g., characteristics of the treatment apparatus 70), to determinethe one or more probabilities of the user complying with the one or moreexercise regimens.

At 808, the processing device may transmit a treatment plan to acomputing device. The computing device may be any suitable interfacedescribed herein. For example, the treatment plan may be transmitted tothe assistant interface 94 for presentation to a healthcare provider,and/or to the patient interface 50 for presentation to the patient. Thetreatment plan may be generated based on the one or more probabilitiesand the respective measure of benefit the one or more exercises mayprovide to the user. In some embodiments, as described further belowwith reference to the method 1000 of FIG. 10, while the user uses thetreatment apparatus 70, the processing device may control, based on thetreatment plan, the treatment apparatus 70.

In some embodiments, the processing device may generate, using at leasta subset of the one or more exercises, the treatment plan for the userto perform, wherein such performance uses the treatment apparatus 70.The processing device may execute the one or more trained machinelearning models 13 to generate the treatment plan based on therespective measure of the benefit the one or more exercises provide tothe user, the one or more probabilities associated with the usercomplying with each of the one or more exercise regimens, or somecombination thereof. For example, the one or more trained machinelearning models 13 may receive the respective measure of the benefit theone or more exercises provide to the user, the one or more probabilitiesof the user associated with complying with each of the one or moreexercise regimens, or some combination thereof as input and output thetreatment plan.

In some embodiments, during generation of the treatment plan, theprocessing device may more heavily or less heavily weight theprobability of the user complying than the respective measure of benefitthe one or more exercise regimens provide to the user. During generationof the treatment plan, such a technique may enable one of the factors(e.g., the probability of the user complying or the respective measureof benefit the one or more exercise regimens provide to the user) tobecome more important than the other factor. For example, if desirableto select exercises that the user is more likely to comply with in atreatment plan, then the one or more probabilities of the userassociated with complying with each of the one or more exercise regimensmay receive a higher weight than one or more measures of exercisebenefit factors. In another example, if desirable to obtain certainbenefits provided by exercises, then the measure of benefit an exerciseregimen provides to a user may receive a higher weight than the usercompliance probability factor. The weight may be any suitable value,number, modifier, percentage, probability, etc.

In some embodiments, the processing device may generate the treatmentplan using a non-parametric model, a parametric model, or a combinationof both a non-parametric model and a parametric model. In statistics, aparametric model or finite-dimensional model refers to probabilitydistributions that have a finite number of parameters. Non-parametricmodels include model structures not specified a priori but insteaddetermined from data. In some embodiments, the processing device maygenerate the treatment plan using a probability density function, aBayesian prediction model, a Markovian prediction model, or any othersuitable mathematically-based prediction model. A Bayesian predictionmodel is used in statistical inference where Bayes' theorem is used toupdate the probability for a hypothesis as more evidence or informationbecomes available. Bayes' theorem may describe the probability of anevent, based on prior knowledge of conditions that might be related tothe event. For example, as additional data (e.g., user compliance datafor certain exercises, characteristics of users, characteristics oftreatment apparatuses, and the like) are obtained, the probabilities ofcompliance for users for performing exercise regimens may becontinuously updated. The trained machine learning models 13 may use theBayesian prediction model and, in preferred embodiments, continuously,constantly or frequently be re-trained with additional data obtained bythe artificial intelligence engine 11 to update the probabilities ofcompliance, and/or the respective measure of benefit one or moreexercises may provide to a user.

In some embodiments, the processing device may generate the treatmentplan based on a set of factors. In some embodiments, the set of factorsmay include an amount, quality or other quality of sleep associated withthe user, information pertaining to a diet of the user, informationpertaining to an eating schedule of the user, information pertaining toan age of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user, anindication of an energy level of the user, or some combination thereof.For example, the set of factors may be included in the training dataused to train and/or re-train the one or more machine learning models13. For example, the set of factors may be labeled as corresponding totreatment data indicative of certain measures of benefit one or moreexercises provide to the user, probabilities of the user complying withthe one or more exercise regimens, or both.

FIG. 9 shows an example embodiment of a method 900 for generating atreatment plan based on a desired benefit, a desired pain level, anindication of a probability associated with complying with theparticular exercise regimen, or some combination thereof, according tosome embodiments. Method 900 includes operations performed by processorsof a computing device (e.g., any component of FIG. 1, such as server 30executing the artificial intelligence engine 11). In some embodiments,one or more operations of the method 900 are implemented in computerinstructions stored on a memory device and executed by a processingdevice. The method 900 may be performed in the same or a similar manneras described above in regard to method 800. The operations of the method900 may be performed in some combination with any of the operations ofany of the methods described herein.

At 902, the processing device may receive user input pertaining to adesired benefit, a desired pain level, an indication of a probabilityassociated with complying with a particular exercise regimen, or somecombination thereof. The user input may be received from the patientinterface 50. That is, in some embodiments, the patient interface 50 maypresent a display including various graphical elements that enable theuser to enter a desired benefit of performing an exercise, a desiredpain level (e.g., on a scale ranging from 1-10, 1 being the lowest painlevel and 10 being the highest pain level), an indication of aprobability associated with complying with the particular exerciseregimen, or some combination thereof. For example, the user may indicatehe or she would not comply with certain exercises (e.g., one-armpush-ups) included in an exercise regimen due to a lack of ability toperform the exercise and/or a lack of desire to perform the exercise.The patient interface 50 may transmit the user input to the processingdevice (e.g., of the server 30, assistant interface 94, or any suitableinterface described herein).

At 904, the processing device may generate, using at least a subset ofthe one or more exercises, the treatment plan for the user to performwherein the performance uses the treatment apparatus 70. The processingdevice may generate the treatment plan based on the user input includingthe desired benefit, the desired pain level, the indication of theprobability associated with complying with the particular exerciseregimen, or some combination thereof. For example, if the user selecteda desired benefit of improved range of motion of flexion and extensionof their knee, then the one or more trained machine learning models 13may identify, based on treatment data pertaining to the user, exercisesthat provide the desired benefit. Those identified exercises may befurther filtered based on the probabilities of user compliance with theexercise regimens. Accordingly, the one or more machine learning models13 may be interconnected, such that the output of one or more trainedmachine learning models that perform function(s) (e.g., determinemeasures of benefit exercises provide to user) may be provided as inputto one or more other trained machine learning models that perform otherfunctions(s) (e.g., determine probabilities of the user complying withthe one or more exercise regimens, generate the treatment plan based onthe measures of benefit and/or the probabilities of the user complying,etc.).

FIG. 10 shows an example embodiment of a method 1000 for controlling,based on a treatment plan, a treatment apparatus 70 while a user usesthe treatment apparatus 70, according to some embodiments. Method 1000includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1000 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1000 maybe performed in the same or a similar manner as described above inregard to method 800. The operations of the method 1000 may be performedin some combination with any of the operations of any of the methodsdescribed herein.

At 1002, the processing device may transmit, during a telemedicine ortelehealth session, a recommendation pertaining to a treatment plan to acomputing device (e.g., patient interface 50, assistant interface 94, orany suitable interface described herein). The recommendation may bepresented on a display screen of the computing device in real-time(e.g., less than 2 seconds) in a portion of the display screen whileanother portion of the display screen presents video of a user (e.g.,patient, healthcare provider, or any suitable user). The recommendationmay also be presented on a display screen of the computing device innear time (e.g., preferably more than or equal to 2 seconds and lessthan or equal to 10 seconds) or with a suitable time delay necessary forthe user of the display screen to be able to observe the display screen.

At 1004, the processing device may receive, from the computing device, aselection of the treatment plan. The user (e.g., patient, healthcareprovider, assistant, etc.) may use any suitable input peripheral (e.g.,mouse, keyboard, microphone, touchpad, etc.) to select the recommendedtreatment plan. The computing device may transmit the selection to theprocessing device of the server 30, which is configured to receive theselection. There may any suitable number of treatment plans presented onthe display screen. Each of the treatment plans recommended may providedifferent results and the healthcare provider may consult, during thetelemedicine session, with the user, to discuss which result the userdesires. In some embodiments, the recommended treatment plans may onlybe presented on the computing device of the healthcare provider and noton the computing device of the user (patient interface 50). In someembodiments, the healthcare provider may choose an option presented onthe assistant interface 94. The option may cause the treatment plans tobe transmitted to the patient interface 50 for presentation. In thisway, during the telemedicine session, the healthcare provider and theuser may view the treatment plans at the same time in real-time or innear real-time, which may provide for an enhanced user experience forthe patient and/or healthcare provider using the computing device.

After the selection of the treatment plan is received at the server 30,at 1006, while the user uses the treatment apparatus 70, the processingdevice may control, based on the selected treatment plan, the treatmentapparatus 70. In some embodiments, controlling the treatment apparatus70 may include the server 30 generating and transmitting controlinstructions to the treatment apparatus 70. In some embodiments,controlling the treatment apparatus 70 may include the server 30generating and transmitting control instructions to the patientinterface 50, and the patient interface 50 may transmit the controlinstructions to the treatment apparatus 70. The control instructions maycause an operating parameter (e.g., speed, orientation, required force,range of motion of pedals, etc.) to be dynamically changed according tothe treatment plan (e.g., a range of motion may be changed to a certainsetting based on the user achieving a certain range of motion for acertain period of time). The operating parameter may be dynamicallychanged while the patient uses the treatment apparatus 70 to perform anexercise. In some embodiments, during a telemedicine session between thepatient interface 50 and the assistant interface 94, the operatingparameter may be dynamically changed in real-time or near real-time.

FIG. 11 shows an example computer system 1100 which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 1100may include a computing device and correspond to the assistanceinterface 94, reporting interface 92, supervisory interface 90,clinician interface 20, server 30 (including the AI engine 11), patientinterface 50, ambulatory sensor 82, goniometer 84, treatment apparatus70, pressure sensor 86, or any suitable component of FIG. 1. Thecomputer system 1100 may be capable of executing instructionsimplementing the one or more machine learning models 13 of theartificial intelligence engine 11 of FIG. 1. The computer system may beconnected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network. The computer system may operate in the capacity ofa server in a client-server network environment. The computer system maybe a personal computer (PC), a tablet computer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a camera, a video camera, an Internet of Things (IoT)device, or any device capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatdevice. Further, while only a single computer system is illustrated, theterm “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methods discussedherein.

The computer system 1100 includes a processing device 1102, a mainmemory 1104 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1106 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1108, which communicate with each other via a bus 1110.

Processing device 1102 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1102 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1102 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1102 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1100 may further include a network interface device1112. The computer system 1100 also may include a video display 1114(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1116 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1118 (e.g., a speaker).In one illustrative example, the video display 1114 and the inputdevice(s) 1116 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1116 may include a computer-readable medium 1120on which the instructions 1122 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1122 may also reside, completely or at least partially, within the mainmemory 1104 and/or within the processing device 1102 during executionthereof by the computer system 1100. As such, the main memory 1104 andthe processing device 1102 also constitute computer-readable media. Theinstructions 1122 may further be transmitted or received over a networkvia the network interface device 1112.

While the computer-readable storage medium 1120 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

Clause 1. A computer-implemented system, comprising:

-   -   a treatment apparatus configured to be manipulated by a user        while performing a treatment plan;    -   a patient interface comprising an output device configured to        present telemedicine information associated with a telemedicine        session; and    -   a processing device configured to:        -   receive treatment data pertaining to the user during the            telemedicine session, wherein the treatment data comprises            one or more characteristics of the user;        -   determine, via one or more trained machine learning models,            at least one respective measure of benefit one or more            exercise regimens provide the user, wherein the determining            the respective measure of benefit is based on the treatment            data;        -   determine, via the one or more trained machine learning            models, one or more probabilities of the user complying with            the one or more exercise regimens; and        -   transmit the treatment plan to a computing device, wherein            the treatment plan is generated based on the one or more            probabilities and the respective measure of benefit the one            or more exercise regimens provide the user.

Clause 2. The computer-implemented system of any clause herein, whereinthe measure of benefit may be positive or negative.

Clause 3. The computer-implemented system of any clause herein, whereinthe processing device is further configured to control, based on thetreatment plan, the treatment apparatus while the user uses thetreatment apparatus.

Clause 4. The computer-implemented system of any clause herein, whereinthe determining the one or more probabilities is based on:

-   -   (i) historical data pertaining to the user, another user, or        both,    -   (ii) received feedback from the user, the another user, or both,    -   (iii) received feedback from the treatment apparatus used by the        user, or    -   (iv) some combination thereof.

Clause 5. The computer-implemented system of any clause herein, whereinthe processing device is further configured to:

-   -   receive user input pertaining to a desired benefit, a desired        pain level, an indication of a probability of complying with a        particular exercise regimen, or some combination thereof; and    -   generate, using at least a subset of the one or more exercises,        the treatment plan for the user to perform using the treatment        apparatus, wherein the generating is further performed based on        the desired benefit, the desired pain level, the indication of        the probability of complying with the particular exercise        regimen, or some combination thereof.

Clause 6. The computer-implemented system of any clause herein, whereinthe processing device is further configured to generate, using at leasta subset of the one or more exercises, the treatment plan for the userto perform using the treatment apparatus, wherein the generating isperformed based on the respective measure of benefit the one or moreexercise regimens provide to the user, the one or more probabilities ofthe user complying with each of the one or more exercise regimens, orsome combination thereof.

Clause 7. The computer-implemented system of any clause herein, whereinthe treatment plan is generated using a non-parametric model, aparametric model, or a combination of both the non-parametric model andthe parametric model.

Clause 8. The computer-implemented system of any clause herein, whereinthe treatment plan is generated using a probability density function, aBayesian prediction model, a Markovian prediction model, or any othermathematically-based prediction model.

Clause 9. The computer-implemented system of any clause herein, whereinthe processing device is further configured to:

-   -   generate the treatment plan based on a plurality of factors        comprising an amount of sleep associated with the user,        information pertaining to a diet of the user, information        pertaining to an eating schedule of the user, information        pertaining to an age of the user, information pertaining to a        sex of the user, information pertaining to a gender of the user,        an indication of a mental state of the user, information        pertaining to a genetic condition of the user, information        pertaining to a disease state of the user, information        pertaining to a microbiome from one or more locations on or in        the user, an indication of an energy level of the user, or some        combination thereof.

Clause 10. The computer-implemented system of any clause herein, whereinthe treatment data further comprises one or more characteristics of thetreatment apparatus.

Clause 11. A computer-implemented method for optimizing a treatment planfor a user to perform using a treatment apparatus, thecomputer-implemented method comprising:

-   -   receiving treatment data pertaining to the user, wherein the        treatment data comprises one or more characteristics of the        user;    -   determining, via one or more trained machine learning models, a        respective measure of benefit one or more exercise regimens        provide the user, wherein the determining the respective measure        of benefit is based on the treatment data;    -   determining, via the one or more trained machine learning        models, one or more probabilities of the user complying with the        one or more exercise regimens; and    -   transmitting the treatment plan to a computing device, wherein        the treatment plan is generated based on the one or more        probabilities and the respective measure of benefit the one or        more exercise regimens provide the user.

Clause 12. The computer-implemented method of any clause herein, whereinthe measure of benefit may be positive or negative.

Clause 13. The computer-implemented method of any clause herein, furthercomprising controlling, based on the treatment plan, the treatmentapparatus while the user uses the treatment apparatus.

Clause 14. The computer-implemented method of any clause herein, whereinthe determining the one or more probabilities is based on:

-   -   (i) historical data pertaining to the user, another user, or        both,    -   (ii) received feedback from the user, the another user, or both,    -   (iii) received feedback from the treatment apparatus used by the        user, or    -   (iv) some combination thereof.

Clause 15. The computer-implemented method of any clause herein, furthercomprising:

-   -   receiving user input pertaining to a desired benefit, a desired        pain level, an indication of a probability of complying with a        particular exercise regimen, or some combination thereof and    -   generating, using at least a subset of the one or more        exercises, the treatment plan for the user to perform using the        treatment apparatus, wherein the generating is further performed        based on the desired benefit, the desired pain level, the        indication of the probability of complying with the particular        exercise regimen, or some combination thereof.

Clause 16. The computer-implemented method of any clause herein, furthercomprising generating, using at least a subset of the one or moreexercises, the treatment plan for the user to perform using thetreatment apparatus, wherein the generating is performed based on therespective measure of benefit the one or more exercise regimens provideto the user, the one or more probabilities of the user complying witheach of the one or more exercise regimens, or some combination thereof.

Clause 17. The computer-implemented method of any clause herein,wherein, during generation of the treatment plan, the probability of theuser complying is weighted more heavily or less heavily than therespective measure of benefit the one or more exercise regimens providethe user.

Clause 18. The computer-implemented method of any clause herein, whereinthe treatment plan is generated using a non-parametric model, aparametric model, or a combination of both the non-parametric model andthe parametric model.

Clause 19. The computer-implemented method of any clause herein, whereinthe treatment plan is generated using a probability density function, aBayesian prediction model, a Markovian prediction model, or any othermathematically-based prediction model.

Clause 20. The computer-implemented method of any clause herein, furthercomprising:

-   -   generating the treatment plan based on a plurality of factors        comprising an amount of sleep associated with the user,        information pertaining to a diet of the user, information        pertaining to an eating schedule of the user, information        pertaining to an age of the user, information pertaining to a        sex of the user, information pertaining to a gender of the user,        an indication of a mental state of the user, information        pertaining to a genetic condition of the user, information        pertaining to a disease state of the user, an indication of an        energy level of the user, or some combination thereof.

Clause 21. The computer-implemented method of any clause herein, whereinthe treatment data further comprises one or more characteristics of thetreatment apparatus.

Clause 22. A non-transitory, computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive treatment data pertaining to a user, wherein the        treatment data comprises one or more characteristics of the        user;    -   determine, via one or more trained machine learning models, a        respective measure of benefit one or more exercise regimens        provide the user, wherein the determining the respective measure        of benefit is based on the treatment data;    -   determine, via the one or more trained machine learning models,        one or more probabilities of the user complying with the one or        more exercise regimens; and    -   transmit a treatment plan to a computing device, wherein the        treatment plan is generated based on the one or more        probabilities and the respective measure of benefit the one or        more exercise regimens provide the user.

Clause 23. The computer-readable medium of any clause herein, whereinthe measure of benefit may be positive or negative.

Clause 24. The computer-readable medium of any clause herein, whereinthe processing device is further configured to control, based on thetreatment plan, a treatment apparatus while the user uses the treatmentapparatus.

Clause 25. The computer-readable medium of any clause herein, whereinthe determining the one or more probabilities is based on:

-   -   (i) historical data pertaining to the user, another user, or        both,    -   (ii) received feedback from the user, the another user, or both,    -   (iii) received feedback from a treatment apparatus used by the        user, or    -   (iv) some combination thereof.

Clause 26. The computer-readable medium of any clause herein, whereinthe processing device is further configured to:

-   -   receive user input pertaining to a desired benefit, a desired        pain level, an indication of a probability of complying with a        particular exercise regimen, or some combination thereof; and    -   generate, using at least a subset of the one or more exercises,        the treatment plan for the user to perform using a treatment        apparatus, wherein the generating is further performed based on        the desired benefit, the desired pain level, the indication of        the probability of complying with the particular exercise        regimen, or some combination thereof.

Clause 27. The computer-readable medium of any clause herein, whereinthe processing device is further configured to generate, using at leasta subset of the one or more exercises, the treatment plan for the userto perform using a treatment apparatus, wherein the generating isperformed based on the respective measure of benefit the one or moreexercise regimens provide to the user, the one or more probabilities ofthe user complying with each of the one or more exercise regimens, orsome combination thereof.

Clause 28. A system comprising:

-   -   a memory device storing instructions; and    -   a processing device communicatively coupled to the memory        device, the processing device executes the instructions to:    -   receive treatment data pertaining to a user, wherein the        treatment data comprises one or more characteristics of the        user;    -   determine, via one or more trained machine learning models, a        respective measure of benefit one or more exercise regimens        provide the user, wherein the determining the respective measure        of benefit is based on the treatment data;    -   determine, via the one or more trained machine learning models,        one or more probabilities of the user complying with the one or        more exercise regimens; and    -   transmit a treatment plan to a computing device, wherein the        treatment plan is generated based on the one or more        probabilities and the respective measure of benefit the one or        more exercise regimens provide the user.

Clause 29. The system of any clause herein, wherein the processingdevice is further configured to:

-   -   receive user input pertaining to a desired benefit, a desired        pain level, an indication of a probability of complying with a        particular exercise regimen, or some combination thereof; and    -   generate, using at least a subset of the one or more exercises,        the treatment plan for the user to perform using a treatment        apparatus, wherein the generating is further performed based on        the desired benefit, the desired pain level, the indication of        the probability of complying with the particular exercise        regimen, or some combination thereof.

Clause 30. The system of any clause herein, wherein the processingdevice is further configured to generate, using at least a subset of theone or more exercises, the treatment plan for the user to perform usinga treatment apparatus, wherein the generating is performed based on therespective measure of benefit the one or more exercise regimens provideto the user, the one or more probabilities of the user complying witheach of the one or more exercise regimens, or some combination thereof.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

What is claimed is:
 1. A computer-implemented system, comprising: atreatment apparatus configured to be manipulated by a user whileperforming a treatment plan; a patient interface comprising an outputdevice configured to present telemedicine information associated with atelemedicine session; and a processing device configured to: receivetreatment data pertaining to the user during the telemedicine session,wherein the treatment data comprises one or more characteristics of theuser; determine, via one or more trained machine learning models, atleast one respective measure of benefit one or more exercise regimensprovide the user, wherein the determining the respective measure ofbenefit is based on the treatment data; determine, via the one or moretrained machine learning models, one or more probabilities of the usercomplying with the one or more exercise regimens; and transmit thetreatment plan to a computing device, wherein the treatment plan isgenerated based on the one or more probabilities and the respectivemeasure of benefit the one or more exercise regimens provide the user.2. The computer-implemented system of claim 1, wherein the measure ofbenefit may be positive or negative.
 3. The computer-implemented systemof claim 1, wherein the processing device is further configured tocontrol, based on the treatment plan, the treatment apparatus while theuser uses the treatment apparatus.
 4. The computer-implemented system ofclaim 1, wherein the determining one or more probabilities is based on:(i) historical data pertaining to the user, another user, or both, (ii)received feedback from the user, the another user, or both, (iii)received feedback from the treatment apparatus used by the user, or (iv)some combination thereof.
 5. The computer-implemented system of claim 1,wherein the processing device is further configured to: receive userinput pertaining to a desired benefit, a desired pain level, anindication of a probability of complying with a particular exerciseregimen, or some combination thereof; and generate, using at least asubset of the one or more exercises, the treatment plan for the user toperform using the treatment apparatus, wherein the generating is furtherperformed based on the desired benefit, the desired pain level, theindication of the probability of complying with the particular exerciseregimen, or some combination thereof.
 6. The computer-implemented systemof claim 1, wherein the processing device is further configured togenerate, using at least a subset of the one or more exercises, thetreatment plan for the user to perform using the treatment apparatus,wherein the generating is performed based on the respective measure ofbenefit the one or more exercise regimens provide to the user, the oneor more probabilities of the user complying with each of the one or moreexercise regimens, or some combination thereof.
 7. Thecomputer-implemented system of claim 6, wherein the treatment plan isgenerated using a non-parametric model, a parametric model, or acombination of both the non-parametric model and the parametric model.8. The computer-implemented system of claim 6, wherein the treatmentplan is generated using a probability density function, a Bayesianprediction model, a Markovian prediction model, or any othermathematically-based prediction model.
 9. The computer-implementedsystem of claim 1, wherein the processing device is further configuredto: generate the treatment plan based on a plurality of factorscomprising an amount of sleep associated with the user, informationpertaining to a diet of the user, information pertaining to an eatingschedule of the user, information pertaining to an age of the user,information pertaining to a sex of the user, information pertaining to agender of the user, an indication of a mental state of the user,information pertaining to a genetic condition of the user, informationpertaining to a disease state of the user, information pertaining to amicrobiome from one or more locations on or in the user, an indicationof an energy level of the user, or some combination thereof.
 10. Thecomputer-implemented system of claim 1, wherein the treatment datafurther comprises one or more characteristics of the treatmentapparatus.
 11. A computer-implemented method for optimizing a treatmentplan for a user to perform using a treatment apparatus, thecomputer-implemented method comprising: receiving treatment datapertaining to the user, wherein the treatment data comprises one or morecharacteristics of the user; determining, via one or more trainedmachine learning models, at least one respective measure of benefit oneor more exercise regimens provide the user, wherein the determining therespective measure of benefit is based on the treatment data;determining, via the one or more trained machine learning models, one ormore probabilities of the user complying with the one or more exerciseregimens; and transmitting the treatment plan to a computing device,wherein the treatment plan is generated based on the one or moreprobabilities and the respective measure of benefit the one or moreexercise regimens provide the user.
 12. The computer-implemented methodof claim 11, wherein the measure of benefit may be positive or negative.13. The computer-implemented method of claim 11, further comprisingcontrolling, based on the treatment plan, the treatment apparatus whilethe user uses the treatment apparatus.
 14. The computer-implementedmethod of claim 11, wherein the determining the one or moreprobabilities is based on: (i) historical data pertaining to the user,another user, or both, (ii) received feedback from the user, the anotheruser, or both, (iii) received feedback from the treatment apparatus usedby the user, or (iv) some combination thereof.
 15. Thecomputer-implemented method of claim 11, further comprising: receivinguser input pertaining to a desired benefit, a desired pain level, anindication of a probability of complying with a particular exerciseregimen, or some combination thereof; and generating, using at least asubset of the one or more exercises, the treatment plan for the user toperform using the treatment apparatus, wherein the generating is furtherperformed based on the desired benefit, the desired pain level, theindication of the probability of complying with the particular exerciseregimen, or some combination thereof.
 16. The computer-implementedmethod of claim 11, further comprising generating, using at least asubset of the one or more exercises, the treatment plan for the user toperform using the treatment apparatus, wherein the generating isperformed based on the respective measure of benefit the one or moreexercise regimens provide to the user, the one or more probabilities ofthe user complying with each of the one or more exercise regimens, orsome combination thereof.
 17. The computer-implemented method of claim16, wherein, during generation of the treatment plan, the probability ofthe user complying is weighted more heavily or less heavily than therespective measure of benefit the one or more exercise regimens providethe user.
 18. The computer-implemented method of claim 16, wherein thetreatment plan is generated using a non-parametric model, a parametricmodel, or a combination of both the non-parametric model and theparametric model.
 19. The computer-implemented method of claim 16,wherein the treatment plan is generated using a probability densityfunction, a Bayesian prediction model, a Markovian prediction model, orany other mathematically-based prediction model.
 20. Thecomputer-implemented method of claim 11, further comprising: generatingthe treatment plan based on a plurality of factors comprising an amountof sleep associated with the user, information pertaining to a diet ofthe user, information pertaining to an eating schedule of the user,information pertaining to an age of the user, information pertaining toa sex of the user, information pertaining to a gender of the user, anindication of a mental state of the user, information pertaining to agenetic condition of the user, information pertaining to a disease stateof the user, information pertaining to a microbiome from one or morelocations on or in the user, an indication of an energy level of theuser, or some combination thereof.
 21. The computer-implemented methodof claim 11, wherein the treatment data further comprises one or morecharacteristics of the treatment apparatus.
 22. A non-transitory,computer-readable medium storing instructions that, when executed, causea processing device to: receive treatment data pertaining to a user,wherein the treatment data comprises one or more characteristics of theuser; determine, via one or more trained machine learning models, atleast one respective measure of benefit one or more exercise regimensprovide the user, wherein the determining the respective measure ofbenefit is based on the treatment data; determine, via the one or moretrained machine learning models, one or more probabilities of the usercomplying with the one or more exercise regimens; and transmit atreatment plan to a computing device, wherein the treatment plan isgenerated based on the one or more probabilities and the respectivemeasure of benefit the one or more exercise regimens provide the user.23. The computer-readable medium of claim 22, wherein the measure ofbenefit may be positive or negative.
 24. The computer-readable medium ofclaim 22, wherein the processing device is further configured tocontrol, based on the treatment plan, a treatment apparatus while theuser uses the treatment apparatus.
 25. The computer-readable medium ofclaim 22, wherein the determining the one or more probabilities is basedon: (i) historical data pertaining to the user, another user, or both,(ii) received feedback from the user, the another user, or both, (iii)received feedback from a treatment apparatus used by the user, or (iv)some combination thereof.
 26. The computer-readable medium of claim 22,wherein the processing device is further configured to: receive userinput pertaining to a desired benefit, a desired pain level, anindication of a probability of complying with a particular exerciseregimen, or some combination thereof; and generate, using at least asubset of the one or more exercises, the treatment plan for the user toperform using a treatment apparatus, wherein the generating is furtherperformed based on the desired benefit, the desired pain level, theindication of the probability of complying with the particular exerciseregimen, or some combination thereof.
 27. The computer-readable mediumof claim 22, wherein the processing device is further configured togenerate, using at least a subset of the one or more exercises, thetreatment plan for the user to perform using a treatment apparatus,wherein the generating is performed based on the respective measure ofbenefit the one or more exercise regimens provide to the user, the oneor more probabilities of the user complying with each of the one or moreexercise regimens, or some combination thereof.
 28. A system comprising:a memory device storing instructions; and a processing devicecommunicatively coupled to the memory device, the processing deviceexecutes the instructions to: receive treatment data pertaining to auser, wherein the treatment data comprises one or more characteristicsof the user; determine, via one or more trained machine learning models,at least one respective measure of benefit one or more exercise regimensprovide the user, wherein the determining the respective measure ofbenefit is based on the treatment data; determine, via the one or moretrained machine learning models, one or more probabilities of the usercomplying with the one or more exercise regimens; and transmit atreatment plan to a computing device, wherein the treatment plan isgenerated based on the one or more probabilities and the respectivemeasure of benefit the one or more exercise regimens provide the user.29. The system of claim 28, wherein the processing device is furtherconfigured to: receive user input pertaining to a desired benefit, adesired pain level, an indication of a probability of complying with aparticular exercise regimen, or some combination thereof; and generate,using at least a subset of the one or more exercises, the treatment planfor the user to perform using a treatment apparatus, wherein thegenerating is further performed based on the desired benefit, thedesired pain level, the indication of the probability of complying withthe particular exercise regimen, or some combination thereof.
 30. Thesystem of claim 28, wherein the processing device is further configuredto generate, using at least a subset of the one or more exercises, thetreatment plan for the user to perform using a treatment apparatus,wherein the generating is performed based on the respective measure ofbenefit the one or more exercise regimens provide to the user, the oneor more probabilities of the user complying with each of the one or moreexercise regimens, or some combination thereof.